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Stimulus-dependent variability and noise correlations in cortical MT neurons

Lookup NU author(s): Professor Alexander Thiele

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Abstract

Population codes assume that neural systems represent sensory inputs through the firing rates of populations of differently tuned neurons. However, trial-by-trial variability and noise correlations are known to affect the information capacity of neural codes. Although recent studies have shown that stimulus presentation reduces both variability and rate correlations with respect to their spontaneous level, possibly improving the encoding accuracy, whether these second order statistics are tuned is unknown. If so, second-order statistics could themselves carry information, rather than being invariably detrimental. Here we show that rate variability and noise correlation vary systematically with stimulus direction in directionally selective middle temporal (MT) neurons, leading to characteristic tuning curves. We show that such tuning emerges in a stochastic recurrent network, for a set of connectivity parameters that overlaps with a single-state scenario and multi-stability. Information theoretic analysis shows that second-order statistics carry information that can improve the accuracy of the population code.

Publication metadata

Author(s): Ponce-Alvarez A, Thiele A, Albright TD, Stoner GR, Deco G

Publication type: Article

Publication status: Published

Journal: Proceedings of the National Academy of Sciences

Year: 2013

Volume: 110

Issue: 32

Pages: 13162-13167

Print publication date: 01/08/2013

ISSN (print): 0027-8424

ISSN (electronic): 1091-6490

Publisher: National Academy of Sciences

URL: http://dx.doi.org/10.1073/pnas.1300098110

DOI: 10.1073/pnas.1300098110

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